Automated method for quantitative measurement of benefits in a plurality of self-assessing, knowledge sharing communities

ABSTRACT

The invention describes method and a system to measure benefits associated with the sharing of knowledge in a plurality of hierarchical knowledge sharing communities. The 5 measurements are based on assessments provided by the members of the community and the usage of the assets and are an index of the benefits arising from the knowledge shared within, and the knowledge contributed to and derived from the members of, the community, at individual and collective levels. The invention also describes a hierarchical arrangement of the knowledge assets and the members in various knowledge 10 sharing communities as well as an incentivisation scheme for the members.

FIELD OF THE INVENTION

The present invention pertains in general to knowledge management incollaborative knowledge sharing communities; more particularly, itrefers to a measurement system for determining the benefits of knowledgesharing in such communities and providing incentives in return for thecontribution made to the community.

BACKGROUND OF THE INVENTION

As knowledge management has come to acquire a pervasive and ubiquitousfunction in business, academic, government, social and personal life, ithas become increasingly important to be able to develop a system ofmeasurement to quantify the benefits associated with the process ofsharing knowledge through interactions between individuals who form partof communities structured either explicitly or implicitly for thepurpose of sharing knowledge. Further, these benefit measures can act asan incentive to the members of the community and thereby enableincreased effectiveness in the sharing of knowledge through implied andreal benefits accrued at the individual and collective levels. Ingeneral, communities can be envisioned as being structured in ahierarchical form, comprising sub-communities with different andspecific foci, and super-communities arising from a logical aggregationof different communities. Given such a structure, there is acorresponding need to be able to carry out benefit measurements to thesharing of knowledge within these hierarchical entities. Such a systemof measurement would readily benefit knowledge management in anycommunity where the members belong either to an organization such as acorporation, a university, a government department, a professionalassociation, and so on, or in a community that is formed for thespecific purpose of sharing knowledge in an area of interest. Themeaning of a community would also encompass various special-interestgroups, discussion forums, chat groups, and the like. While theinvention in its generality covers all forms of knowledge sharing, it ismost readily applicable where knowledge sharing happens through on-linemechanisms.

A knowledge sharing community generally, although not exclusively,comprises people with common areas of interest, common intents andpurposes for sharing, and having a collectively accepted protocol forthe form, structure and content of the knowledge that is shared betweenthe members of the community, as well as the modes of interaction whichwould aid the process of sharing knowledge. The employees of anInformation Technology (IT) company who use a computer network to shareknowledge in the form of documents of different kinds, as for examplerelated to, software technologies, software project managementprocesses, people oriented human relations (HR) practices, etc wouldexemplify a knowledge sharing community. In this example, the usualconnotations associated with a community—of its members being relatedthrough geography and kinship—can be understood in a broader way toinclude geographically distributed membership and kinship engendered bya common purpose related to the company's activities. The commonality ofareas of interest could exist at different levels of abstraction withoutaffecting the meaning of the word community, as in the example above,where a developer of software systems could primarily contribute to, andbenefit from, the knowledge management system in the areas oftechnologies without necessarily having any relationship with othercomponents of the system which may be directed towards projectmanagement or people related practices, etc. The protocols for sharingknowledge could be different for each community. For example, acommunity of ophthalmologists could share knowledge primarily throughphysical participation in periodic lectures, seminars, surgical orclinical demonstrations, and publication of newsletters; whereas acommunity of Java developers could share knowledge primarily throughparticipation in virtual modes of interaction, viz., chat sessions,online reviews of publications, discussion forums, and collaborativeevolution of standards, technologies and co-development of new idioms ofprogramming, packages, products and proofs-of-concepts among itsmembers.

Knowledge assets constitute a primary transactional medium for theknowledge shared between members of a community. Here, no limitationsand constraints are implied in the form, structure or manifestation ofthe knowledge being shared except to the extent of common acceptabilitywithin, and systemic constraints applicable to, the community sharingknowledge. Thus, for instance, documents forming the basis of knowledgesharing could be either in physical or electronic forms; also, knowledgesharing itself could be carried out without documents of any kind, as inthe case of brain storming sessions, workshops, off-line discussionsbetween groups of people, one-to-one mentoring in face to faceinteractions, and the like. Knowledge assets therefore can in general beconsidered to be content of various kinds presented in different ways—asexemplified by documents, audio and video clips, publications, recordsof debates, discussions, workshops, interactive lectures, knowledgesharing sessions, white boarding and on-line collaboration sessions,telephonic conferences, video conferences, chat sessions, expertiseprofiles (which provides knowledge about knowledge, in this case, ofexpertise among members of a community), and so forth.

The measurement of the benefits of sharing knowledge is of significancein determining the functionality and health of the knowledge sharingcommunity's practices, in addition to being an index of the maturity andvalue associated with the sum total knowledge resident in the communityat a given point in time. The benefit measurements in turn would be apowerful means to elicit contributions from members on a continuousbasis. In general, the benefits could cover those derived from, andprovided to, hierarchical communities, each community itself and anindividual who is a part of one or more communities. To be able todevelop an objective system to quantitatively measure the benefits ofknowledge sharing, a consideration of the entire life cycle of knowledgecapture, structuring, sharing and use and so forth would be offundamental importance. Capturing knowledge could be done in a varietyof ways—through documents such as white papers, manuals, tools,operational procedures and processes, etc submitted by members, orcreated through interviews with facilitators, transcription ofdiscussions, recording of knowledge sharing sessions in audio, video orsome other electronic form, on-line records of chat sessions anddiscussion forums, etc. Matching its characteristics with thoseassociated with a classification system appropriate to the particularknowledge sharing activity, in general, we can carry out the structuringof such captured knowledge. The actual process of sharing and use amongthe members of the community would then provide the basis for rating thedifferent knowledge assets available within the community. This could bedone with two measures, one through user assigned ratings, transmittedfor recording to a system maintained by the community to track ratings,of a knowledge asset as may be related to its perceived quality,utility, relevance, applicability, and so on. The other would be throughsystem calculated parameters related to the knowledge sharingtransaction, such as, the number of times the asset has been used,recency and frequency of use, time of use, and the like, either inrelation to the past use of the said asset in isolation or in relationto all, or some, of the other knowledge assets available for use withthe community, as may be determined by the community. These parameterscan be used to modulate the ratings assigned to the asset and thecontributor of the knowledge asset per transaction, as well as points,if any, assigned to the user for the usage of the knowledge asset. Itmust be noted here that, in their generality, ratings given are basedonly on an assumption of a beneficiary-benefactor relationship in theact of sharing knowledge. These ratings of assets, in turn, could beused to order the assets themselves in specific logical/physical ways tocater to subsequent requests for assets of that particular type. Forinstance, the rating of a document could be used as the basis forretrieving a document with the highest rating on the topic requested forby a user. In addition, the rating obtained by the benefactor andbeneficiary of the transactions could in turn be translatable topre-defined rewards provided by the community. For purposes of settingpolicies and providing directions, such as may be related to theredemption of points accrued by members for rewards, the determinationof time periods during which redemption is permitted and/or notpermitted and so on, the community could designate, perhaps following ademocratic process, certain members as moderators from time to time.

However, not all knowledge assets available to the community may berequired to be rated. In general, the community would decide whatknowledge assets are subject to rating, and by whom in the community.For instance, a knowledge asset such as an expertise profile may betreated by a community as not subject to rating; as for documents, thecommunity may allow all users to rate a document, so long as it is firstreviewed (a review may be considered here as a special type of userrating) and accepted for depositing it with the community for allmembers to use. Similarly, the community may have a policy forretirement of documents which may be related to the document'sattributes—content, age, frequency and recency of use, utility, and thelike.

Various methods and systems have been proposed at a generic level forknowledge management in a business organization. U.S. Pat. No. 5,924,072for Knowledge Management System and Method to Charnell T. Havens, U.S.Pat. No. 6,182,067 for Methods and Systems for Knowledge Management toPresnell et al., WO 01/08096 for Knowledge Management System to DeniseL. Holz, and WO 98/32083 for Knowledge Management System and Method toAnthony D. Sullivan all disclose knowledge management methods andsystems that cover the core ideas of a repository of knowledge that isshared across an organization with methods for adding new knowledgeitems to the repository and for retrieving relevant items from it aswell as methods to provide feedback on retrieved items and to collectusage data for knowledge items.

It must be noted that the term “knowledge management system” is alsoused to describe disclosures of information storage, retrieval andsearch systems such as in WO 97/21179 to Butler et al. The term is alsoused in disclosures of systems for managing knowledge bases for expertsystems and artificial intelligence as in WO 99/66420 to Guignard et al.These two uses of the term are not directly relevant to the presentdisclosure.

Several disclosures in the prior art have proposed metrics to measurethe value of a knowledge item in an organization. Some of these metricsare based on usage statistics for the particular knowledge item,including the frequency and recency of usage of the item by members ofthe organization (e.g., U.S. Pat. No. 5,924,072, U.S. Pat. No. 6,182,067and U.S. Pat. No. 5,079,718). Similarly, metrics based on subjectiveevaluations of knowledge items by its users have been proposed in U.S.Pat. No. 5,924,072 and U.S. Pat. No. 6,182,067.

The idea of using ratings assigned by selected reviewers to measure thevalue of knowledge items has been disclosed in U.S. Pat. No. 5,706,452to Ivanov and WO 01/08096 to Denise L. Holz. These metrics constituteanother dimension of value that is useful in a knowledge managementsystem.

A concept-based organization of knowledge assets together with a searchand retrieval engine that provides a concept-match based technique toselect knowledge items is ideally suited to knowledge management systems(as opposed to keyword matching systems). In addition, the idealknowledge management system must also provide a plurality of hierarchiesof concepts along which the user can navigate to browse the knowledgestored against the hierarchies. Such concept-based retrieval systems aredisclosed for example in U.S. Pat. No. 6,182,067 and U.S. Pat. No.6,327,593 to David A. Goiffon. Multi-dimensional hierarchies (alsocalled taxonomies) and their use in organizing and retrieving knowledgeitems are disclosed in U.S. Pat. No. 6,327,593 to Goiffon, WO 01/08096to Holz and WO 00/77690 to Kay et al.

It may be noted here that a simpler alternative to multi-dimensionalhierarchies in the form of two-dimensional matrices, as disclosed in WO97/21179 to Butler is not adequate for knowledge management purposes ina typical organization where the usage of a knowledge item cannot bepredetermined and directly mapped to particular business processes ortypes of users.

Several disclosures propose the idea of retiring knowledge items iftheir value falls below a certain threshold to save storage space aswell as to make it easier for users to find current items. For exampleU.S. Pat. No. 5,079,718 to Tanaka discloses a “cancel function” which isessentially a retirement mechanism (akin to a computer memory managementalgorithm) based entirely on usage statistics. U.S. Pat. No. 6,182,067to Presnell discloses a retirement method based on usage statistics aswell as subjective user evaluations.

A key issue in deploying a knowledge management system in anorganization lies in providing incentives to its members to participatein effective knowledge management (such as to share their knowledge orto re-use knowledge shared by others). At a generic level, U.S. Pat. No.5,924,072 to Havens discloses that subjective user evaluations and usagestatistics can be used to provide incentives to authors of knowledgeitems.

A key problem in knowledge management is to measure progress indeploying a knowledge management solution in an organization. Anotherkey problem is to measure benefits of using knowledge management. Bothof these require metrics for determining the current ratings of not onlyknowledge items but also of members of the organization with referenceto their contributions to knowledge management as well as ratings ofentire groups or sub-communities in the organization. There is no clearand comprehensive set of metrics available in prior art to meet theserequirements.

Further, the prior art disclosures do not provide a comprehensive metricthat combines reviewer ratings with metrics derived from userevaluations and usage statistics. It is not clear from the prior art asto how the various metrics could be combined. In addition, selecting anappropriate reviewer for a new or revised knowledge item continues to bea problem with no clear method for systematic or automatic selection

A typical large knowledge sharing community needs to be organized into ahierarchy of sub-communities for effective management of knowledge invarious areas of interest or focus. There is no method in the prior artthat discloses a metric for measuring progress and benefits of knowledgemanagement in such a hierarchy of communities.

SUMMARY OF THE INVENTION

According to the present invention, the disadvantages and problemsassociated with prior art in knowledge management methods, systems, andprograms have been substantially reduced or eliminated.

It is a primary objective of the present invention to disclose acomprehensive set of metrics for measuring the value that knowledgeitems, individual members, and entire communities and sub-communitiesprovide to an organization through knowledge management.

It is another objective of the present invention to have a method oforganizing a set of communities and sub-communities against amulti-dimensional knowledge hierarchy and thereby be able to calculatethe said metrics for a community or sub-community at any level in thehierarchies.

It is a further objective of the present invention to disclose a methodof selecting reviewers for a knowledge item by matching the knowledgepaths in the knowledge hierarchies for the knowledge item with knowledgepaths in the same hierarchies that denote the areas of expertise ofrecognized members of the community.

It is yet another objective of the present invention to disclose amethod for calculating and providing a variety of incentives to membersof the community including both milestone rewards and proportionateredemptions based on the said metrics.

The present invention provides a system to measure benefits associatedwith the sharing of knowledge in a plurality of hierarchical knowledgesharing communities. In such communities, various types of knowledgesharing interactions are envisaged at the formal, semi-formal orinformal levels, the only condition being that corresponding knowledgeassets be identified and provided in an electronic or other forms tofurther the activities of the community. A transaction based device andquantitative rating system to capture, classify, store, use, share,encourage, rate, rank, prioritize and retire some or all of theintellectual assets in a knowledge sharing community is disclosed. Therating system would measure benefits associated with the knowledgeassets, beneficiaries and contributors in the community as well as thecommunity itself. Further, the measurements are based on assessmentsprovided by the members of the community resulting in an incremental,evolving metric of all aspects of knowledge sharing including thequality and potential utility of the knowledge assets owned by thecommunity, expertise of members and the benefits derived by thecommunity as well as its members.

The invention details methods for calculating these metrics at any pointin time based on usage statistics, subjective user evaluations, as wellas reviewer ratings. The metrics can be used to track progress andmeasure benefits of knowledge management.

For the purposes of the current invention, a member of a sub-communitywould be automatically considered a member of a community, but notvice-versa. For instance, in a community of programming languageenthusiasts, there could be sub-communities for Java and C# pronounced Csharp) as languages. The member of the Java sub-community (or the C#community) would be considered a member of the programming languagecommunity, but not vice-versa. Conversely, one can be a member of acommunity without being a member of any sub-community. A member of onecommunity would be free to become a member of any other community. Whena member ends his/her association with a community, he/she is consideredto be an ex-member; the active (and not retired) knowledge assets of theex-member would continue to be rated by the members of the community,although any ratings ascribed to the ex-member as the creator of theknowledge assets would not accrue to the aggregate rating of theex-member. The hierarchy of communities referred to in the presentinvention makes no assumption regarding the existence of a universalcommunity.

A community whose members themselves measure the knowledge sharingbetween the members would constitute a self-assessing community. In suchcommunities, a continual assessment of the knowledge sharing by allmembers would occur during the normal course of use. Thus, assessmentthrough rating of the knowledge shared is an emergent property of suchcommunities where there are likely to be no clear demarcation of rolessuch as leaders, assigned reviewers and so on. With the condition that amember can provide only one rating to a knowledge asset at a given time,continuous and incremental changes over time to the ratings of knowledgeassets can result from new rating by members who have not rated theasset earlier, revision of old rating (due to changes in perceived valueof an asset) by members, and so forth. Further, the rating metric,aggregated over different transactions at the member, sub-community andthe community levels, would itself be a time-wise dynamic measure ofbenefits, an index of functionality and value, as well as an incentivefor contribution and use.

For the purposes of this invention, a community per se would beunderstood as one that conducts its activities without explicitcommercial terms (other than membership fees) for both contribution andusage of the knowledge contributed directly, or derived consequentially,from the activities of the members in whatever form as related to thecommunity; further, capture of knowledge from members (at leastpartially), sharing of knowledge, and assessing the benefits of suchsharing, by members are key requirements for such communities. It is,however, important to note that the definition above does not precludeindividuals from using the community's shared knowledge in turn forcommercial purposes so long as the activity of sharing knowledge withinthe community is carried out without any explicit commercial terms.Given this definition, valid examples of communities would therefore benewsgroups, chat groups, professional associations, support groups,organizations such as corporations, university or governmentdepartments, non-government organizations of various kinds, social andvoluntary groups, etc; this definition would exclude communities formedfor the primary purpose of formal education, lawyer-client relationships(including pro-bono relationships), libraries, contests and competitionsof various kinds, internet based (or otherwise) provider-subscriberservices such as paid magazines, bulletins, news wires, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the various components of aknowledge repository.

FIG. 2 illustrates a multi-dimensional knowledge hierarchy.

FIG. 3 illustrates the publication system.

FIG. 4 shows an overview of the review and rating system.

FIG. 5 illustrates the knowledge asset rating calculator.

FIG. 6 illustrates the member rating calculator.

FIG. 7 illustrates the search and retrieval system.

FIG. 8 illustrates the points redemption system.

FIG. 9 shows an overview of the preferred embodiment.

FIG. 10 shows the general computer network on which the invention mightbe practiced.

FIG. 11 shows the basic internal structure of the computing devices thatmake up the computer network as described in FIG. 10.

Appendix A shows snapshots of computer screens from the preferredembodiment.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a Knowledge Repository. Theknowledge repository is a knowledge database (1.2) arranged in anhierarchical (1.1) manner. Knowledge database comprises a collection ofKnowledge Assets (1.3). A knowledge asset is any document, a thread ofmessages in a discussion, an expertise profile of a member, or atranscript or record of a session of collaboration among members. Adocument can be an article, a whitepaper, a presentation, a webpage, apiece of program code, a spreadsheet, or any write-up in any languageand in any format. Documents are stored either in printed form or in acomputer file system. Documents can be stored in a central documentrepository (1.4) or in any of a plurality of satellite documentrepositories (1.5). Each repository has a hierarchy of folders andsub-folders that classify the documents according to a predeterminedclassification scheme (1.6).

Further each knowledge asset has associated meta data (1.7). Meta-datacontains information about knowledge assets, members, about the usage ofknowledge assets by members and about knowledge-sharing interactionsbetween members.

Meta data in the preferred embodiment comprises:

K-Asset Meta-Data: Attributes

Each knowledge asset is identified by a primary key which is a uniquealphanumeric serial number assigned to that asset. In addition, eachasset has a (plurality of) member(s) who created the asset, a URL orother location descriptor that identifies the normal location of theasset in the repositories, the date and time when the asset waspublished in the repository, and a (plurality of) knowledge path(s) fromthe knowledge hierarchy that constitute(s) the classification of theasset in the knowledge repository. In addition, a knowledge asset maycontain a variety of other attributes that are of interest to theknowledge sharing community, such as a title, an abstract, and a set ofkeywords.

K-Asset Meta-Data: Usage and Ratings

Each knowledge asset has a composite rating at any point of time. Inaddition, ratings given to it by any member at any point in time, alongwith any private and public comments given by the member, and date andtime the rating was given are stored. A rating is any numeric value in arange that is predefined for the type of knowledge asset.

Member Meta-Data: Attributes

Each member is identified by a unique primary key which is analphanumeric serial number assigned to that member. In addition, amember's name and contact information, such as the member's email id,telephone numbers, and addresses may be stored. The member'saffiliations with various departments and organizations may also bestored here.

A member has a composite rating at any point of time. In addition, ifredemption of rewards is included, the portion of the member's ratingthat is eligible for redemption is stored.

A member may also be associated with a profile which comprises themember's qualifications, prior experiences relevant to the community,areas of expertise, memberships in other communities, and subscriptionsto classes of knowledge assets.

Member Meta-Data: Usage and Ratings

All ratings given by members to a particular member are stored alongwith the receiving member's meta-data. Ratings can be for documentswritten by the member, discussion threads to which the member hadcontributed, or collaboration sessions where the member shared knowledgewith other members.

Community Meta-Data: Attributes

Each community has a unique primary key, which is an alphanumeric serialnumber assigned to that community. A community also has a name and an (aplurality of) identified owner(s) among the members of the community.Optionally, a community may also have sub-communities and a (pluralityof) moderator(s) from among the members of the community.

Community Meta-Data: Usage and Ratings

Each community has an aggregate rating derived from the summation of theratings given to all its members. Usage of the community's knowledge byother communities is also part of the community's meta-data. Such usagecan be derived from usage of the community's documents or knowledgesharing in collaboration sessions by members of other communities in theorganization.

Knowledge Hierarchy defines the classification scheme for the knowledgedatabase. It is a multi-dimensional hierarchical arrangement of topicsand other attributes that are of interest to the knowledge-sharingcommunity. The knowledge hierarchy may contain multiple directed acyclicgraphs for each dimension of classification. Dimensions may be broadtopic areas (2.4), type of document (e.g., article or webpage orsoftware code) (2.1), language, format (2.3), or any other parameter ofinterest to the community. It might also define the target audience ofthe document (2.2). As an example of a knowledge area (2.4), Technologycomprises the subareas of Databases, Internet Technology and the like.Further the subarea Database might contain areas such as SQL, Oracle andthe like. A particular path from a root node to a given node, called aKnowledge Path (2.5), rather than simply the node itself, uniquelyidentifies a particular classification.

FIG. 2 shows a knowledge hierarchy from the preferred embodimentcomprising the four dimensions of content type (2.1), document format(2.3), target audience (2.2), and knowledge area (2.4). Knowledge areasare classified into a four-level hierarchy comprising six separatedirected acyclic graphs. A knowledge asset is classified by assigning toit one or more knowledge paths in the knowledge hierarchy. An embodimentmay mandate some types of knowledge assets to be classified alongspecific dimensions. For example, every document must belong to one andonly one content type. The set of knowledge paths assigned to aknowledge asset determines its type. All knowledge assets having thesame knowledge paths in a particular dimension are said to be of thesame type in that dimension. For example, all whitepapers are of thesame type.

A knowledge repository also defines the range of ratings allowed foreach type of knowledge asset. For example, whitepapers may have a rangeof 0 to 10 while book reviews have a range of 0 to 4. A knowledge assetneed not be stored in a separate computer file. For example, discussionthreads may be stored either in individual computer files or together ina computer database (called Discussion Thread Repositories in FIG. 1).Similarly, expertise profiles of members, although considered knowledgeassets, may be stored entirely in the database along with all meta-dataabout members.

FIG. 3 is a flowchart illustrating the publication system of a knowledgeasset. Members (3.1) submit knowledge assets and the correspondingmeta-data attributes (3.2). Each asset is authored by one or moremembers. At the time of submission, authors provide a mapping from theknowledge asset to one or more nodes in the knowledge hierarchy alongvarious dimensions of the knowledge hierarchy, such as its content type,topic, or target audience.

Other members of the community can see the submissions once they arepublished on the knowledge repository (3.5). Publication may optionallyrequire a validation (3.3) and a review (3.9). If it does not require avalidation the asset is published (3.4) in the knowledge repository(3.5). Else the asset is validated (3.6). The asset is checked todetermine if it fulfills the validation criteria (3.7). If it does notfulfill the validation criteria then the asset is rejected and theconcerned member/s is/are informed (3.8).

Validation may be for the purpose of checking sanity, readability,authenticity, originality, possible violation of intellectual propertyrights of others, etc. Validation is done by an assigned member of thecommunity (such as its leader, moderator) or by an assigned contenteditor.

An asset is checked to determine if it needs to be reviewed (3.9). Ifnot, then the asset is published (3.4) in the knowledge repository(3.5). If the asset needs to be reviewed then the reviewer(s) areidentified and notified (3.10). The reviewer(s) assign(s) comments andratings to the asset (3.11) and also determines whether the asset needsto be changed (3.12) before it can be published. If changes arenecessary, concerned member(s) is/are notified (3.8). Else, the asset ispublished (3.4) in the repository (3.5).

Knowledge assets are published with a default rating at a predeterminedpercentage of the maximum rating allowed for the type of the knowledgeasset. For example, the percentage of default rating may be 60%. Areview may also include an optional quality review by an (a pluralityof) assigned reviewer(s). The quality review may also result inrejecting the submission. A rejected knowledge asset is not available toother members of the community. Quality reviews are done by assignedreviewers who are also members of the community. Quality reviews may berequired before publication or it may happen after publication,depending on the type of knowledge asset and the decisions made by theleader, moderator, or members of the community.

Reviewers assign a rating to the knowledge asset they review in a rangeof values with a maximum value that is predetermined for the type of theknowledge asset. Once an assigned reviewer gives a rating to a knowledgeasset, its default rating is voided. If multiple reviewers rate aknowledge asset, the arithmetic mean of their ratings is applied tocalculate the overall rating of the knowledge asset.

Reviewers can demand changes to the knowledge asset. In such a case, themoderator, editor, or the author(s) of the knowledge asset must make thechanges and resubmit the knowledge asset even if it is alreadypublished. In such cases, earlier versions of the knowledge asset willno longer be accessible to members of the community.

An author may also choose to submit a new version of an alreadypublished knowledge asset. The new version goes through the samevalidation, review, and publication process. A community may decide tomake both the new version and older versions of a knowledge assetavailable to the members of the community, or it may decide to publishonly the latest version of a knowledge asset (sometimes depending on thetype of the knowledge asset).

FIG. 4 shows an overview of how ratings are calculated for knowledgeassets, members, and communities. The point rating system allows membersof the community to rate knowledge assets by awarding points onpredetermined scales. These points are awarded by the reviewers (4.1) aswell as members of the community (4.2). These points are storedalongwith the comments and time of rating (4.3). The points thus awardedare used for the calculation of aggregate ratings of both members (4.5)and knowledge assets (4.4). Member ratings are further aggregated tocalculate community ratings (4.6).

FIG. 5 shows the knowledge asset composite rating calculator thatcomputes a rating P_(k-asset) for a published knowledge asset based onthe ratings awarded to the asset by reviewers (5.1) and members (5.3) ofthe community. The calculator also takes into account the number oftimes the knowledge asset is used (5.6) to calculate the frequencyfactor (5.7). It also calculates the recency factor (5.5). In step 5.2,the arithmetic mean of the ratings given by reviewers to the knowledgeasset, normalized to a scale of 0 to 10, is the aggregate rating fromreviewers P_(reviewer). Similarly in step 5.4, the arithmetic mean ofthe ratings by members, normalized to a scale of 0 to 10, is theaggregate rating from members P_(member). The recency factorP_(frequency) is computed (5.5) by reducing successive older ratingsawarded by members to the K-asset by associating progressivelydiminishing weights with each such rating, such weights governed by anequation that ensures smoothness in their decrements. The frequencyfactor P_(frequency) is computed (5.7) based on the number of times theknowledge asset is used relative to all other published assets of thesame type.

The composite of all these ratings (5.8) forms the rating for theknowledge asset (5.9).

In the preferred embodiment, the composite rating of a knowledge assetis computed by the following formula:P_(k-asset) = 0.4 * P_(reviewer) + 0.3 * P_(member) + 0.1 * P_(recency) + 0.2 * P_(frequency)where  P_(frequency) = (1 − 𝕖^(−λ  x)) * 10 where$x = {{{usage}\quad{index}} = \frac{\#\quad{ratings}{\quad\quad}{for}{\quad\quad}{the}\quad K\text{-}{asset}}{{Average}( {\#\quad{ratings}{\quad\quad}{for}\quad K\text{-}{assets}{\quad\quad}{of}\quad{same}{\quad\quad}{type}} )}}$

-   -   where, X=a constant

P_(recency) is computed by considering time windows of equal duration (3months in the preferred embodiment). The number of time windowsrepresented by “n”, will be in the range 1 to 6. Ratings awarded bymembers are grouped within the boundaries of each time window i and therespective arithmetic means R_(i) calculated. Each mean is associatedwith progressively diminishing weights but maintaining the sum ofweights as 1. The weighted average of ratings from all relevant timewindows is P_(recency).

For example, if the current window is weighted by a number a,0.5<=a<=1.0,the previous consecutive window will be weighted by a*(1−a) and the nextsuccessive older period as a*(1−a)² and so on. In other words, theweights are decremented in an exponential manner, the last weight being1−[a+a*(1−a)+a(1−a)² + . . . a*(1−a)^((n−1))]

If there is a time window for which there is no member rating for theasset, the mean rating belonging to the more recent window successive tothe empty window is used for the calculation. In case there are only twosuccessive time windows available, the weights assigned will be “a” forthe current window and “(1−a)” for the past one.P _(recency) =a*R ₁ +a*(1−a)*R ₂ + . . . +a*(1−a)^(i−1) *R _(i)+ . . .+{1−[a+a(1−a)+ . . . +a(1−a)^((n−1)) ]}*R _(n)

FIG. 6 shows the member rating calculator which aggregates the pointsearned by a member through one or more of the following means:

-   -   1. The sum P_(member) _(—) _(rating) of the ratings given by        other members to a K-asset authored by the member (6.2).    -   2. The sum P_(reviewer) _(—) _(rating) of the ratings given by        reviewers to a K-asset authored by the member (6.1).    -   3. The sum P_(review) of the points earned by the member for        reviewing K-assets of submitted by other members (6.3).    -   4. The sum P_(rating) of the points earned by the member for        rating published K-assets available in the community (6.4).

The aggregate rating of the member (6.6) is calculated (6.5) as:P _(member) =P _(reviewer) _(—) _(rating) +P _(member) _(—) _(rating) +P_(review) +P _(rating)

These points could be redeemed by the member (6.7) and also be displayed(6.8).

The aggregate rating of an entire community is calculated as the sum ofthe aggregate ratings of all members of the community. If a memberbelongs to more than one community or to a community and several of itssub-communities, the member has separate ratings in each community orsub-community.

FIG. 7 shows the search and retrieval system. In a keyword-based searchsystem (7.2), documents are retrieved in the order of their relevance toa member's (7.1) query. The knowledge hierarchy outlined in thisinvention where knowledge assets are organized and retrieved using amultidimensional knowledge hierarchy, search keywords can be combinedwith knowledge paths (7.4) selected by the user from the knowledgehierarchy. Those knowledge assets are selected (7.6) that contain thekeywords (7.3) and also match the selected knowledge paths (7.5).Selected knowledge assets can be ordered based on the relativesignificance of assets as measured by their composite ratings in thecommunity (7.7).

FIG. 8 shows the points redemption system. The knowledge repositorycontains a scoreboard that shows every member's accumulated points. Thescoreboard also shows the aggregate scores of the entire community andany of its sub-communities thereby providing recognition to both membersand sub-communities. The scoreboard also shows a break-up of scores intothose obtained by being authors of knowledge assets, those obtained byreviewing other members' assets, and those obtained for providingcollaboration in knowledge sharing sessions. A member can see bothhis/her scores as well as the scores of some of the highest scorers inthe community.

In the preferred embodiment, members can redeem their accumulated pointsto obtain various rewards predetermined by the community. For example,points can be redeemed for cash, gift certificates, or other materialgoods. The member's total score of points will continue to be the sameafter redemption although a point can be redeemed only once for sometypes of rewards. For example, redemption in exchange for cash or itsequivalents can be done only once, but the same points can beaccumulated and redeemed multiple times to obtain plaques orcertificates for predetermined milestones in points.

FIG. 8 shows the point redemption system. As shown in 8.1 a member withrating ‘n’ and with ‘a’ points redeemed earlier, has ‘m’ redeemablepoints where m=n−min−a, where ‘min’ is the minimum number of pointspredetermined by the community, only above which the member can redeempoints. It may be noted that the term ‘m’ is required to be positive(i.e., m>0) for redemption of points by the member to be possible. Aftera member redeems ‘x’ points where x<=m (8.2) for generating rewardsequivalent to ‘x’ (8.3) the redeemed points are updated as ‘a+x’ andredeemable as ‘m-x’ (8.4).

In the preferred embodiment, the knowledge management system in a globalinformation technology services company has set up sub-communities alongorganizational divisions such as departments and practice units for itscommunity of employees across the globe. The communities apply themetric disclosed here to measure the utility of knowledge assets asperceived by employees of the community and also to rate thecontributions of members and sub-communities to knowledge sharing withinthe company. The aggregate rating of the entire community is used as ameasure of progress in implementing knowledge management across thecompany. All employees are connected through a network of computersacross all locations where the company operates. This computer networkis isolated from the Internet by firewalls to form an intranet. Membersaccess knowledge assets and submit new assets through a central websiteon the intranet that acts as a knowledge portal. Members can also searchand identify members who are experts in particular topics. Members cancommunicate with each other through electronic mails, telephones, andthrough on-line discussion forums for various topics. Every member has aunique employee number as well as an e-mail id that can be readily foundin an on-line employee directory. Every member has a set oforganizational attributes that include their employment grade, theircurrent roles such as developers, business managers, etc., and theiraffiliations with projects, departments, and business units.

The knowledge repository is hosted on the knowledge portal. Therepository stores the files that contain the knowledge and all themeta-data in a relational database. The Knowledge hierarchy used in thisembodiment is shown in FIG. 2. Knowledge assets are classified byassigning a content type, a target audience, and 1 to 6 knowledge pathsin the topic dimension. Some knowledge assets also have keywords andabstracts associated with them.

All employees have an on-line profile which they can voluntarily modifyto include their current expertise.

Review and ratings mechanisms and the calculations of various aggregateratings in this embodiment have been described in the DetailedDescriptions of figures above.

In this embodiment, members can redeem their points above a minimum of25 points to obtain electronic gift certificates. These certificates canbe used at an e-commerce site on the Internet to purchase books, music,or other artifacts and services.

FIG. 9 shows a general block diagram of the preferred embodimentimplementing the above mentioned publishing and review schemes for theknowledge assets. The core of the system is formed by a KnowledgeRepository (9.1). Members (9.2) submit the knowledge asset to thisdatabase through submission interface (9.3). Some of these members whoare assigned as reviewers (9.5) can award/revise points (9.6) orenter/revise (9.7) comments to the knowledge asset in the repository.Further, members can query (9.9)_the knowledge repository (9.1) througha search engine (9.4) to retrieve the knowledge assets relevant to them.The member points are also stored in the Knowledge Repository databasethat is used by a Points Scoreboard (9.8) to display the member points.

FIG. 10 shows a general computer network (10.6) on which the inventionmight be practiced. It consists of a bunch of servers (10.1, 10.2, 10.3)interconnected by any known communication means such as by wired means,radio links or infrared transmissions. The networking topology could beany known one in the art like star, linear, ring and the like, or acombination of these. Further, in order to communicate, these serverscould use any of the known communication protocols such as TCP/IP,Ethernet and the like. These servers could either have a dedicatedstorage (10.4) or two or more servers might share a storage (10.5). Userworkstations (10.7, 10.8, 10.9) are connected to the network (10.6) andcontact one or more servers for the retrieval of the data storedtherein. Here too, the interconnection could be through any means,topology and follow any protocol. Further, the user workstations can beindirectly connected to the network by virtue of being interconnected toeach other. The computer network could be any type of network, public orprivate. Using a combination of these networks it is possible to makefull or part of the information stored on the servers available to aroaming user. There could be specific servers on the public network orone can connect to the private network through a public network usingtechnologies such as Virtual Private Network. Further the number ofservers and the workstations is not limited and the data can resideeither on one server or it could be distributed over a number ofservers. Also some specific data can reside on the user workstationsalso.

The invention can be practiced on any general computer system. Theclients and servers could be any computing device. The computer systemcomprises a display device with a display screen Examples of displaydevice are Cathode Ray Tube (CRT) devices, Liquid Crystal Display (LCD)devices etc. The computer system can also have other additional outputdevices like a printer. The cabinet houses the additional essentialcomponents of the computer system such as the microprocessor, memory anddisk drives. In a general computer system the microprocessor is anycommercially available processor for which ×86 processors from Intel and680X0 series from Motorola are examples. Many other microprocessors areavailable. The computer system could be a single processor system or mayuse two or more processors on a single system or over a network. Themicroprocessor for its functioning uses a volatile memory that is arandom access memory such as dynamic random access memory (DRAM) orstatic memory (SRAM). The disk drives are the permanent storage mediumused by the computer system. This permanent storage could be a magneticdisk, a flash memory and a tape. This storage could be removable like afloppy disk or permanent such as a hard disk. Besides this, the cabinetcan also house other additional components like a Compact Disc Read OnlyMemory (CD-ROM) drive, sound card, video card etc. The computer systemalso has various input devices like a keyboard and a mouse. The keyboardand the mouse are connected to the computer system through wired orwireless links. The mouse could be a two-button mouse, three-buttonmouse or a scroll mouse. Besides the said input devices there could beother input devices like a light pen, a track ball etc. Themicroprocessor executes a program called the operating system for thebasic functioning of the computer system. The examples of operatingsystems are UNIX, WINDOWS and DOS. These operating systems allocate thecomputer system resources to various programs and help the users tointeract with the system. It should be understood that the invention isnot limited to any particular hardware comprising the computer system orthe software running on it.

The servers are generally high end computing system with greaterreliability and speed whereas the user workstations could be electronicdevices like personal computers, mobile phones, interactive televisionsand the like.

FIG. 11 shows the internal structure of the general computer system asdescribed above. The computer system (11.1) consists of varioussubsystems interconnected with the help of a system bus (11.2). Themicroprocessor (11.3) communicates and controls the functioning of othersubsystems. Memory (11.4) helps the microprocessor in its functioning bystoring instructions and data during its execution. Permanent Storage(11.5) is used to hold the data and instructions permanent in naturelike the operating system and other programs. Display adapter (11.6) isused as an interface between the system bus and the display device(11.7), which is generally a monitor. The network interface (11.8) isused to connect the computer with other computers on a network throughwired or wireless means. The computer system might also contain a soundcard (not shown). The system is connected to various input devices likekeyboard (11.9) and mouse (11.10) and output devices like printer(11.11). Various configurations of these subsystems are possible. Itshould also be noted that a system implementing the present inventionmight use less or more number of the subsystems than described above.

Those of ordinary skill in the art will appreciate that the variousmeans described are instructions for operating on the computing system.The means are capable of existing in an embedded form within thehardware of the system or may be embodied on various computer readablemedia. The computer readable media may take the form of coded formatsthat are decoded for actual use in a particular information processingsystem. Computer program means, or a computer program in the presentcontext means, any expression, in any language, code, or notation, of aset of instructions intended to cause a system having informationprocessing capability to perform the particular function either directlyor after performing either or both of the following:

a) conversion to another language, code or notation

b) reproduction in a different material form.

The depicted example in FIGS. 10 and 11 is not meant to implyarchitectural limitations and the configuration of the incorporatingdevice of the said means may vary depending on the implementation. Anykind of computer system or other apparatus adapted for carrying out themeans described herein can be employed for practicing the invention. Atypical combination of hardware and software could be a general purposecomputer system with a computer program that when loaded and executed,controls the computer system such that it carries out the meansdescribed herein. Other examples of the incorporating device that may beused are notebook computers or hand held computers in addition topersonal digital assistants (PDAs), web kiosks or even Web appliances.

1. An automated method for measuring benefits accruing from themanagement of knowledge in a self-assessing knowledge sharing community,or a plurality of communities and sub-communities said method comprisingthe steps of: storing knowledge assets in a repository, preferably in acomputer-readable format, cataloguing of knowledge assets for easyretrieval by classifying them against a multi-dimensional knowledgehierarchy, receiving new knowledge assets from members of the community,validating, reviewing and rating of the new knowledge assets by assignedmembers of the community, storing and publishing the validated knowledgein the repository, reviewing and rating of published knowledge assets byany member of the community, calculating a composite rating forknowledge assets based on an aggregation of ratings and usage over timeof the knowledge assets in the community, calculating an aggregaterating for a member in each community based on the contributions of themember to the community, calculating an aggregate rating for eachcommunity based on the ratings of all its members, and calculating anddisplaying on a scoreboard, various ratings for members, communities,and sub-communities.
 2. The method as claimed in claim 1, whereinknowledge assets include documents, discussion threads, profiles ofexperts, and records of knowledge sharing sessions.
 3. (canceled)
 4. Themethod as claimed in claim 1, wherein cataloguing of knowledge assets isdetermined by a multidimensional knowledge hierarchy used for theclassification of all types of knowledge assets in the knowledgerepository including expert profiles, for the selection of reviewers forreviewing submitted assets, and for the organization of sub-communitiesinto communities.
 5. (canceled)
 6. The method as claimed in claim 1,wherein the rating and reviewing of knowledge assets by assigned membersat the time of publishing comprises the steps of: selecting one or morereviewers by matching the knowledge nodes and paths of the asset withthose of the expert profiles of members in the community, using theknowledge hierarchy, assigning of ratings to the knowledge assets by thereviewer(s), and entering of comments by the reviewer visible to allmembers of the community as well as private comments visible only to theauthor(s) of the knowledge asset.
 7. (canceled)
 8. The method as claimedin claim 6, wherein the step of assignment of ratings by a reviewer tothe knowledge asset includes: normalizing the rated points to a scale ofpoints whose range is determined by the type of the knowledge asset,and, awarding the rated points to each of the author(s) of the knowledgeasset either in their entirety or with an apportionment in the case of aplurality of authors.
 9. The method as claimed in claim 6, wherein apredetermined fraction of the maximum number of points possible in therange of points for the type of the reviewed knowledge asset is accruedto the reviewer(s).
 10. The method as claimed in claim 1, whereinreviewing and rating of a knowledge asset after publishing can beconducted by any member of the community other than an author orreviewer of the knowledge asset along with comments that are accessibleto all members of the community.
 11. (canceled)
 12. (canceled)
 13. Themethod as claimed in claim 10, wherein the ratings assigned by a memberto a knowledge asset includes: normalizing the rated points to a scaleof points whose range is determined by the type of the knowledge asset,and, accrual of the rated points to each of the authors of the knowledgeasset either in their entirety or with an apportionment in case of aplurality of authors.
 14. The method as claimed in claim 10, wherein apredetermined fraction of the maximum number of points possible in therange of points for the type of the knowledge asset rated is accrued tothe member who contributes the rating.
 15. The method as claimed inclaim 1, wherein calculating the composite ratings of knowledge assetscomprises the steps of calculating, reviewer ratings as the arithmeticmean of all the reviewer ratings given to the knowledge asset, memberratings as the arithmetic mean of all the member ratings given to theknowledge asset, frequency of usage as a fraction in exponentialrelation to the ratio of the number of times the knowledge asset is usedby members to the arithmetic mean of the numbers of times all knowledgeassets of the same type in the community are used by members, recency ofusage as a discrete integral over time of the product of the arithmeticmean of the ratings given to the knowledge asset by members in a window,from a set of windows of equal time intervals, and a correspondingfraction from a predetermined set of time-varying fractions that sum upto 1.0. the weighted sum of the reviewer rating, member rating,frequency of usage and recency of usage, where the weight for reviewerrating is greater than the weight for member rating and the sum of thefour weights is equal to 1.0, the composite rating for the knowledgeasset as the weighted sum normalized to a predetermined scale.
 16. Themethod as claimed in claim 15 wherein the weighted sum is normalized ona scale of 1 to
 10. 17. (canceled)
 18. The method as claimed in claim 1,wherein calculating the aggregate rating of a member or a former memberin a particular community further comprises the steps of calculating:reviewer accruals as zero for former members and, for current members,as the sum of the points, over all reviews of knowledge assets done bythe member in the community or any of its sub-communities, member ratingaccruals as zero for former members and, for current members, as the sumof the points, over all ratings of knowledge assets in the community orany of its sub-communities done by the member, author accruals fromreviewers as the sum of the points, over all reviews of knowledge assetsauthored by the member in the community or any of its sub-communities,author accruals from reviewers as the sum of the points, over allreviews of knowledge assets authored by the member in the community orany of its sub-communities, author accruals from members as the sum ofthe points, over all ratings of knowledge assets authored by the memberin the community or any of its sub-communities, and the aggregate ratingas the sum of the reviewer accruals, member rating accruals, authoraccruals from reviewers and author accruals from members.
 19. (canceled)20. (canceled)
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 37. (canceled)38. An automated system for measuring benefits accruing from themanagement of knowledge in a self-assessing knowledge sharing community,or a plurality of communities and sub-communities, comprising: arepository for storing knowledge assets preferably in acomputer-readable format, a catalogue for cataloguing the knowledgeassets for easy retrieval by classifying them against amulti-dimensional knowledge hierarchy, means for receiving new knowledgeassets from members of the community, means for validating, reviewingand rating of the new knowledge assets by assigned members of thecommunity, means for storing and publishing the validated knowledge inthe repository, means for reviewing and rating of published knowledgeassets by any member of the community, calculating means for a compositerating for knowledge assets based on an aggregation of ratings and usageover time of the knowledge assets in the community, calculating meansfor an aggregate rating for a member in each community based on thecontributions of the member to the community, calculating means for anaggregate rating for each community based on the ratings of all itsmembers, and means for calculating and displaying on a scoreboardvarious ratings for members, communities, and sub-communities. 39.(canceled)
 40. (canceled)
 41. (canceled)
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 43. (canceled)44. (canceled)
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 58. (canceled)
 59. A computer program product comprisingcomputer readable program code stored on computer readable storagemedium embodied therein for providing an automated system for measuringbenefits accruing from the management of knowledge in a self-assessingknowledge sharing community, or a plurality of communities andsub-communities, comprising: computer readable program code configuredfor storing knowledge assets in a repository, preferably in acomputer-readable format, computer readable program code configured forcataloguing the knowledge assets for easy retrieval by classifying themagainst a multi-dimensional knowledge hierarchy, computer readableprogram code configured for receiving new knowledge assets from membersof the community, computer readable program code configured forvalidating, reviewing and rating of the new knowledge assets by assignedmembers of the community, computer readable program code configured forstoring and publishing the validated knowledge in the repository,computer readable program code configured for reviewing and rating ofpublished knowledge assets by any member of the community, computerreadable program code configured for calculating a composite rating forknowledge assets based on an aggregation of ratings and usage over timeof the knowledge assets in the community, computer readable program codeconfigured for calculating an aggregate rating for a member in eachcommunity based on the contributions of the member to the community,computer readable program code configured for calculating an aggregaterating for each community based on the ratings of all its members, andcomputer readable program code configured for calculating and displayingon a scoreboard various ratings for members, communities, andsub-communities.
 60. (canceled)
 61. (canceled)
 62. (canceled) 63.(canceled)
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 76. (canceled)77. The method as claimed in claim 18, wherein a members currentaggregate rating in a community is made accessible on the scoreboard toall members of the community.
 78. The method as claimed in claim 1,wherein the aggregate rating of a community is calculated as the sum,over all members including former members of the community, of theaggregate ratings of each member in that community.
 79. The method asclaimed in claim 18, wherein the members in a community can redeem theirpoints in exchange for rewards.
 80. The method as claimed in claim 79wherein the redeemable points of a member in a community are calculatedas the sum of the reviewer accruals, author accruals and member ratingaccruals of the member, considering only the knowledge assets in thecommunity and not those in sub-communities.
 81. The method as claimed inclaim 79, wherein a member can redeem some of his/her redeemable pointsto obtain multiple types of rewards at a rate of exchange of points toparticular rewards predetermined by the community.
 82. The method asclaimed in claim 79, wherein multiple types of rewards are awarded tomembers on achieving predetermined milestones in their points in thecommunity.
 83. The system as claimed in claim 38, wherein knowledgeassets include documents, discussion threads, profiles of experts, andrecords of knowledge sharing sessions.
 84. The system as claimed inclaim 38, wherein means for cataloguing of knowledge assets includes amulti-dimensional knowledge hierarchy used as the means for theclassification of all types of knowledge assets in the knowledgerepository including expert profiles, the means for the selection ofreviewers for reviewing submitted assets, and the means for theorganization of sub-communities into communities.
 85. The system asclaimed in claim 38, wherein means for rating and reviewing of knowledgeassets by assigned members at the time of publishing comprises: meansfor selecting one or more reviewers by matching the knowledge nodes andpaths of the asset with those of the expert profiles of members in thecommunity, using the knowledge hierarchy, means for assigning of ratingsto the knowledge assets by the reviewer(s), and means for entering ofcomments by the reviewer visible to all members of the community as wellas private comments visible only to the author(s) of the knowledgeasset.
 86. The system as claimed in claim 85, wherein means forassignment of ratings by a reviewer to the knowledge asset includes:means for normalizing the rated points to a scale of points whose rangeis determined by the type of the knowledge asset, and means for awardingthe rated points to each of the author(s) of the knowledge asset eitherin their entirety or with an apportionment in the case of a plurality ofauthors.
 87. The system as claimed in claim 38, further comprising meansfor reviewing and rating of a knowledge asset after publishing by anymember of the community other than an author or reviewer of theknowledge asset along with comments that are accessible to all membersof the community.
 88. The system as claimed in claim 87, wherein meansfor assigning the rating by a member to a knowledge asset includes:means for normalizing the rated points to a scale of points whose rangeis determined by the type of the knowledge asset, and, means foraccruing of the rated points to each of the authors of the knowledgeasset either in their entirety or with an apportionment in the case of aplurality of authors.
 89. The system as claimed in claim 38, whereinmeans for calculating the composite ratings of knowledge assetscomprises: means for calculating reviewer ratings as the arithmetic meanof all the reviewer ratings given to the knowledge asset, means forcalculating member ratings as the arithmetic mean of all the memberratings given to the knowledge asset, means for calculating frequency ofusage as a fraction in exponential relation to the ratio of the numberof times the knowledge asset is used by members to the arithmetic meanof the numbers of times all knowledge assets of the same type in thecommunity are used by members, means for calculating recency of usage asa discrete integral over time of the product of the arithmetic mean ofthe ratings given to the knowledge asset by members in a window, from aset of windows of equal time intervals, and a corresponding fractionfrom a predetermined set of time-varying fractions that sum up to 1.0,means for calculating the weighted sum of the reviewer rating, memberrating, frequency of usage and recency of usage, where the weight forreviewer rating is greater than the weight for member rating and the sumof the four weights is equal to 1.0, means for calculating the compositerating for the knowledge asset as the weighted sum normalized to apredetermined scale.
 90. The system as claimed in claim 38, whereinmeans for calculating the aggregate rating of a member or a formermember in a particular community further comprises: means forcalculating reviewer accruals as zero for former members and, forcurrent members, as the sum of the points, over all reviews of knowledgeassets done by the member in the community or any of itssub-communities, means for calculating member rating accruals as zerofor former members and, for current members, as the sum of the points,over all ratings of knowledge assets in the community or any of itssub-communities done by the member. means for calculating authoraccruals from reviewers as the sum of the points, over all reviews ofknowledge assets authored by the member in the community or any of itssub-communities, means for calculating author accruals from members asthe sum of the points, over all ratings of knowledge assets authored bythe member in the community or any of its sub-communities, and means forcalculating the aggregate rating as the sum of the reviewer accruals,member rating accruals, author accruals from reviewers and authoraccruals from members.
 91. The system as claimed in claim 38, whereinsaid means wholly or partially reside on a computing system comprisingat least one system bus, at least one communications unit connected tothe system bus, a memory unit including a set of instructions connectedto the system bus, and at least one control unit executing theinstructions in the memory for the functioning of said means.
 92. Thesystem as claimed in claim 91, further connected to other similarsystems and database systems that may contain means to complement andsupplement the already existing means.
 93. The system as claimed inclaim 92, wherein said systems are interconnected through any suitablecomputer network including Ethernet, Internet, LAN, WAN, and MAN usingany desired network topology including ring, bus and star.
 94. Thecomputer program product as claimed in claim 59, wherein knowledgeassets include documents, discussion threads, profiles of experts, andrecords of knowledge sharing sessions.
 95. The computer program productas claimed in claim 59, comprising computer readable program codeconfigured for cataloguing of knowledge against a multi-dimensionalknowledge hierarchy used for the classification of all types ofknowledge assets in the knowledge repository including expert profiles,for the selection of reviewers for reviewing submitted assets, and forthe organization of sub-communities into communities.
 96. The computerprogram product as claimed in claim 59, wherein computer readableprogram code for rating and reviewing of knowledge assets by assignedmembers at the time of publishing comprises: computer readable programcode configured for selecting one or more reviewers by matching theknowledge nodes and paths of the asset with those of the expert profilesof members in the community, using the knowledge hierarchy, computerreadable program code configured for assigning of ratings to theknowledge assets by the reviewer(s), and computer readable program codeconfigured for entering of comments by the reviewer visible to allmembers of the community as well as private comments visible only to theauthor(s) of the knowledge asset.
 97. The computer program product asclaimed in claim 96, wherein computer readable program code forassignment of ratings by a reviewer to the knowledge asset includes:computer readable program code configured for normalizing the ratedpoints to a scale of points whose range is determined by the type of theknowledge asset, and, computer readable program code configured forawarding the rated points to each of the author(s) of the knowledgeasset either in their entirety or with an apportionment in the case of aplurality of authors.
 98. The computer program product as claimed inclaim 59, further comprising computer readable program code configuredfor reviewing and rating of a knowledge asset after publishing by anymember of the community other than an author or reviewer of theknowledge asset along with comments that are accessible to all membersof the community.
 99. The computer program product as claimed in claim98, wherein computer readable program code for assigning the rating by amember to a knowledge asset includes: computer readable program codeconfigured for normalizing the rated points to a scale of points whoserange is determined by the type of the knowledge asset, and, computerreadable program code configured for accruing of the rated points toeach of the authors of the knowledge asset either in their entirety orwith an apportionment in the case of a plurality of authors.
 100. Thecomputer program code as claimed in claim 59, wherein computer readableprogram code for calculating the composite ratings of knowledge assetscomprises: computer readable program code configured for calculatingreviewer ratings as the arithmetic mean of all the reviewer ratingsgiven to the knowledge asset, computer readable program code configuredfor calculating member ratings as the arithmetic mean of all the memberratings given to the knowledge asset, computer readable program codeconfigured for calculating frequency of usage as a fraction inexponential relation to the ratio of the number of times the knowledgeasset is used by members to the arithmetic mean of the numbers of timesall knowledge assets of the same type in the community are used bymembers, computer readable program code configured for calculatingrecency of usage as a discrete integral over time of the product of thearithmetic mean of the ratings given to the knowledge asset by membersin a window, from a set of windows of equal time intervals, and acorresponding fraction from a predetermined set of time-varyingfractions that sum up to 1.0, computer readable program code configuredfor calculating the weighted sum of the reviewer rating, member rating,frequency of usage and recency of usage, where the weight for reviewerrating is greater than the weight for member rating and the sum of thefour weights is equal to 1.0, and computer readable program codeconfigured for calculating the composite rating for the knowledge assetas the weighted sum normalized to a predetermined scale.
 101. Thecomputer program product as claimed in claim 59, wherein computerreadable program code for calculating the aggregate rating of a memberor a former member in a particular community further comprises: computerreadable program code configured for calculating reviewer accruals aszero for former members and, for current members, as the sum of thepoints, over all reviews of knowledge assets done by the member in thecommunity or any of its sub-communities, computer readable program codeconfigured for calculating member rating accruals as zero for formermembers and, for current members, as the sum of the points, over allratings of knowledge assets in the community or any of itssub-communities done by the member, computer readable program codeconfigured for calculating author accruals from reviewers as the sum ofthe points, over all reviews of knowledge assets authored by the memberin the community or any of its sub-communities. computer readableprogram code configured for calculating author accruals from members asthe sum of the points, over all ratings of knowledge assets authored bythe member in the community or any of its sub-communities, and computerreadable program code configured for calculating the aggregate rating asthe sum of the reviewer accruals, member rating accruals, authoraccruals from reviewers and author accruals from members.
 102. Themethod as claimed in claim 1, wherein calculating the aggregate ratingof a member or a former member in a particular community furthercomprises the steps of calculating: reviewer accruals as zero for formermembers and, for current members, as the sum of the points, over allreviews of knowledge assets done by the member in the community or anyof its sub-communities, each such point being conditioned by the recencyand frequency of usage of the asset, as well as the current average ofthe ratings obtained by the asset from members other than the reviewerin the community, member rating accruals as zero for former members and,for current members, as the sum of the points, over all ratings ofknowledge assets in the community or any of its sub-communities done bythe member, each such point being conditioned by the recency andfrequency of usage of the asset, as well as the current average of theratings obtained by the asset from members other than the reviewer inthe community, author accruals from reviewers as the sum of the points,over all reviews of knowledge assets authored by the member in thecommunity or any of its sub-communities, each such point beingconditioned by the recency and frequency of usage of the asset, as wellas the current average of the ratings obtained by the asset from membersother than the reviewer in the community, author accruals from reviewersas the sum of the points, over all reviews of knowledge assets authoredby the member in the community or any of its sub-communities, authoraccruals from members as the sum of the points, over all ratings ofknowledge assets authored by the member in the community or any of itssub-communities, and the aggregate rating as the sum of the revieweraccruals, member rating accruals, author accruals from reviewers andauthor accruals from members.